Published on in Vol 5 (2024)

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2023.01.21.23284795v1, first published .
Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Journals

  1. Dong T, Oronti I, Sinha S, Freitas A, Zhai B, Chan J, Fudulu D, Caputo M, Angelini G. Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects. Bioengineering 2024;11(10):1039 View
  2. Sinha S, Dong T, Dimagli A, Judge A, Angelini G. A machine learning algorithm-based risk prediction score for in-hospital/30-day mortality after adult cardiac surgery. European Journal of Cardio-Thoracic Surgery 2024;66(4) View
  3. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. Journal of Clinical Medicine 2024;13(23):7108 View
  4. Dong T, Sinha S, Angelini G. Reply to Rajakumar. European Journal of Cardio-Thoracic Surgery 2024;67(1) View
  5. Fang L, Wu Y, Yao T, Wang Z, Qian S, Jiang T, Xu J, Lin Y, Li Y. Use of pulse pressure index for cardiovascular outcomes assessment and development of a coronary heart disease model for the elderly. BMC Cardiovascular Disorders 2025;25(1) View